Undergraduate Course: Introduction to Statistics for Social Science- Summer School (SSPS08006)
Course Outline
| School | School of Social and Political Science |
College | College of Humanities and Social Science |
| Course type | Standard |
Availability | Not available to visiting students |
| Credit level (Normal year taken) | SCQF Level 8 (Year 1 Undergraduate) |
Credits | 20 |
| Home subject area | School (School of Social and Political Studies) |
Other subject area | None |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | Introducing basic statistical tools for Students in the With Quantitative Methods Degrees. This is a 2 week conversion course covering the same course content as the standard semester length version.
This course is the introduction to common quantitative techniques and software used in the social sciences. It is designed to meet the needs of students in the with Quantitative Methods degree programmes in SPS, and to provide them with a broad range of basic concepts and methods, which they will later use as the basis for intermediate and advanced quantitative techniques. The course is aimed at students who also study Sociology, Social Policy, Politics, and International Relations. As such, it will contain examples and applications relevant for all these disciplines.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | Higher/A Level Maths at B required |
| Additional Costs | None |
Course Delivery Information
| Not being delivered |
Summary of Intended Learning Outcomes
At the end of this course, students are expected to have the following skills:
-Basic SPSS skills, including graphical skills and skills of presenting summaries of data clearly
-A basic understanding of secondary data access and management
-An understanding of measures of association
-An understanding of inference and the logic of sampling
-Communication of basic statistics
-Appreciation of the difference between association and causality
-An understanding of the concept of control
-Being able to construct 3 way cross-tabulations
-A basic understanding of regression analysis
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Assessment Information
40% mid-course exam (comprised of multiple-choice questions) end of week 1. This constitutes a formative feedback event.
60% continuous assessment in week 2 (students will conduct a series of analysis tasks and report them each morning based on the previous days material).
This assessment would provide the necessary concession for transfer into year 2 of the with QM degree programmes.
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Special Arrangements
| Students are expected to attend the whole 2 weeks, which covers the same material as the semester length version. 1 day is roughly equivalent to 1 week, with half the day given over to supervised lab sessions in place of the independent learning that would be expected during the 11 week version. |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Introduction: Why do quantitative methods? And an introduction to secondary data access and management
Distribution and introduction to SPSS
2: Associations with categorical variables
Contingency tables
Measures of association between categorical variables
Causality and the concept of control in a 3-way contingency table
Part 3: Associations with continuous variables
Bivariate analysis for continuous variables
Introduction to multiple linear regression
Linear regression continued: Dummy variables and interactions
Part 3: The bigger picture: Inference and communicating research
Inference and the logic of sampling
Summary: Communicating quantitative analysis
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| Transferable skills |
Not entered |
| Reading list |
D.M. Diez, C.D. Barr, & M.C. Etinkaya-Rundel (2013) OpenIntro Statistics (2nd edition),
http://www.openintro.org/stat/textbook.php
J. Pallant (2010 4th edition) SPSS Survival Manual, Maidenhead: Open UP
C. Marsh & J. Elliott (2008) Exploring Data (2nd edition), Cambridge: Polity
J. Fielding & N. Gilbert (2006) Understanding Social Statistics (2nd edition), London: Sage
D. Freedman et al. (various editions), Statistics, London: Norton
H. Blalock (various editions), Social Statistics, New York: McGraw-Hill
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| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Alison Koslowski
Tel: (0131 6)51 1147
Email: alison.koslowski@ed.ac.uk |
Course secretary | |
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